Neonatal seizures are the most common neurological emergency in the neonate and are a serious concern for clinicians and parents worldwide. Only about one third of all neonatal seizures are clinically visible and many remain undetected in the busy Neonatal Intensive Care Unit (NICU) environment. The only method available to detect all neonatal seizures accurately is continuous multi-channel EEG monitoring. Interpretation of neonatal EEG requires a neurophysiologist or paediatric neurologist with specific expertise in neonatal EEG. This expertise is not available on a 24 h basis, 7 days a week. To fill the gap in the availability of appropriate expertise, clinical staff in the NICU often use a simpler form of EEG monitoring, known as amplitude integrated EEG (aEEG). Amplitude integrated EEG is an logarithmically-scaled, temporally-smoothed and compressed display of EEG which is usually computed from two EEG channels, one from each hemisphere. Despite the fact that many short and focal neonatal seizures are undetectable with aEEG and inter-observer agreement is poor, aEEG currently serves as a trade-off between very inaccurate clinical detection of seizures and very accurate but scarcely available neurophysiologic expertise, and thus is widely adopted worldwide in the NICU.
In view of the above mentioned problems associated with the use of aEEG, it will be appreciated that an automated decision support system that could detect and annotate seizures on the neonatal EEG would be extremely useful for clinicians in the NICU.
In this regard, it is believed that human hearing input is better than the visual input when it comes to assessing both the spatial and temporal evolution of the frequency characteristics of a signal. Hearing is flexible and low-cost. Hearing also allows for faster processing than visual presentation, has better temporal resolution, and represents an additional information channel, releasing visual sense for other tasks. Therefore, an automated decision support system that could detect and annotate seizures on the neonatal EEG through the use of audio would be desirable.
It is known to audify the EEG signals of adults, for purposes such as to detect epilepsy. One such audification method involves lifting an adult's recorded brain frequencies into the human audible range, by saving the original waveform with a higher sampling rate. In this signal resampling process, time and pitch manipulations are always linked so that time compression scales pitches upwards, time stretching scales pitches downwards. However, with this direct EEG audification, the resultant audio sounds noisy. Furthermore, real-time EEG playback is also not possible.
Another such audification method of adult EEG signals is to map EEG spectral frequencies to the audible range by sonification. In this process, the dominant EEG frequencies are extracted using the fast Fourier transform, and then the tones of the mapped frequencies are created with pre-specified parameters such as duration and pitch. However, it should be appreciated that when sonification is used, what is heard by a listener is not the real EEG, but rather ‘synthetic’ artificially generated audio waveforms which have been extracted from the original EEG content.
Accordingly, an object of the present invention is to provide a method and system for the audification of neonatal EEG signals which overcomes at least one of above mentioned problems associated with existing adult EEG signal audification methods.